Process window optimizer

ABSTRACT

Disclosed herein is a computer-implemented defect prediction method for a device manufacturing process involving processing a pattern onto a substrate, the method comprising: identifying a processing window limiting pattern (PWLP) from the pattern; determining a processing parameter under which the PWLP is processed; and determining or predicting, using the processing parameter, existence, probability of existence, a characteristic, or a combination thereof, of a defect produced from the PWLP with the device manufacturing process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC 119(e) of priorco-pending U.S. Provisional Patent Application No. 61/939,071, filedFeb. 12, 2014 and U.S. Provisional Patent Application No. 61/943,834,filed Feb. 24, 2014, all of which are hereby incorporated by referencein the present disclosure in their entirety.

FIELD

The present invention relates to a method of optimizing the performanceof semiconductor manufacturing process. The method may be used inconnection with a lithographic apparatus.

BACKGROUND

A lithographic apparatus is a machine that applies a desired patternonto a target portion of a substrate. Lithographic apparatus can beused, for example, in the manufacture of integrated circuits (ICs). Inthat circumstance, a patterning device, which is alternatively referredto as a mask or a reticle, may be used to generate a circuit patterncorresponding to an individual layer of the IC, and this pattern can beimaged onto a target portion (e.g. comprising part of, one or severaldies) on a substrate (e.g. a silicon wafer) that has a layer ofradiation-sensitive material (resist). In general, a single substratewill contain a network of adjacent target portions that are successivelyexposed. Known lithographic apparatus include so-called steppers, inwhich each target portion is irradiated by exposing an entire patternonto the target portion in one go, and so-called scanners, in which eachtarget portion is irradiated by scanning the pattern through the beam ina given direction (the “scanning”-direction) while synchronouslyscanning the substrate parallel or anti parallel to this direction.

SUMMARY

Disclosed herein is a computer-implemented defect determination orprediction method for a device manufacturing process involvingprocessing patterns onto a substrate, the method comprising: identifyingone or more processing window limiting patterns (PWLPs) from thepatterns; determining one or more processing parameters under which thePWLPs are processed; determining or predicting, using the one or moreprocessing parameters, existence, probability of existence, one or morecharacteristics, or a combination thereof, of a defect produced from atleast one of the PWLPs with the device manufacturing process. In anembodiment, the one or more processing parameters are determinedimmediately before the PWLPs are processed. In an embodiment, the defectis undetectable before the substrate is irreversibly processed. The factthat the defect is undetectable may be due to the limited quality of theinspection tools used to do the standard inspection. If such a defect ispredicted using the computer-implemented defect prediction methoddisclosed herein, the specific defect may be inspected by a non-standardinspection tool to further assess the severity of the predicted defect.Alternatively, the defect may be too small to be detected at all usingany of the inspection tools available at the time of drafting the text.In such case, the prediction of the defect using the method disclosedherein may be used to decide to rework the die or wafer to avoid thepredicted defect on the product.

According to an embodiment, determining or predicting, using the one ormore processing parameters, existence, probability of existence, one ormore characteristics, or a combination thereof, further uses acharacteristic of the PWLPs, a characteristic of the patterns, or both.

According to an embodiment, the method further comprises adjusting theone or more processing parameters using the existence, the probabilityof existence, the one or more characteristics, or a combination thereof,of the defect. In an embodiment, determining or predicting existence,probability of existence, one or more characteristics, or a combinationthereof, of a defect, and adjusting the one or more processingparameters, may be carried out reiteratively.

According to an embodiment, the method further comprises determining orpredicting, using the adjusted one or more lithographic parameters,existence, probability of existence, one or more characteristics, or acombination thereof, of a residue defect produced from the at least oneof the PWLPs using the device manufacturing process.

According to an embodiment, the method further comprises determiningprocess windows of the PWLPs.

According to an embodiment, the method further comprises compiling theone or more processing parameters into a processing parameters map.

According to an embodiment, the one or more PWLPs are identified usingan empirical model or a computational model.

According to an embodiment, the one or more processing parameters areselected from a group consisting of focus, dose, source parameters,projection optics parameters, data obtained from metrology and data fromoperator of the processing apparatus.

According to an embodiment, the data obtained from metrology areobtained from a diffractive tool, or an electron microscope.

According to an embodiment, the one or more processing parameters aredetermined or predicted using model or by querying a database.

According to an embodiment, determining or predicting the existence, theprobability of existence, the one or more characteristics, or acombination thereof, of the defect comprises comparing the one or moreprocessing parameters with the process windows.

According to an embodiment, determining or predicting the existence, theprobability of existence, the one or more characteristics, or acombination thereof, of the defect comprises using a classificationmodel with the one or more processing parameters as input to theclassification model.

According to an embodiment, the classification model is selected from agroup consisting of logistic regression and multinomial logit, probitregression, the perceptron algorithm, support vector machines, importvector machines, and linear discriminant analysis.

According to an embodiment, determining or predicting the existence, theprobability of existence, the one or more characteristics, or acombination thereof, of the defect comprises simulating an image orexpected patterning contours of at least one of the PWLPs under theprocessing parameters and measuring the image or contour parameters.

According to an embodiment, the device manufacturing process involvesusing a lithography apparatus.

Disclosed herein is a method of manufacturing a device involvingprocessing patterns onto a substrate or onto a die of the substrate, themethod comprising: determining processing parameters before processingthe substrate or the die; predicting or determining existence of adefect, probability of existence of a defect, a characteristic of adefect, or a combination thereof using the processing parameters beforeprocessing the substrate or the die, and using a characteristic of thesubstrate or the die, a characteristic of the geometry of patterns to beprocessed onto the substrate or the die, or both; adjusting theprocessing parameters based on the prediction or determination so as toeliminate, reduce the probability or severity of the defect.

According to an embodiment, the method further comprises identifying oneor more processing window limiting patterns (PWLPs) from the patterns.

According to an embodiment, the defect is a defect produced from atleast one of the PWLPs.

According to an embodiment, the characteristic of the substrate or thedie is a process window of at least one of the PWLPs.

Disclosed herein is a method of manufacturing a device involvingprocessing patterns onto a batch of substrates, the method including:processing the batch of substrates, destructively inspecting less than2%, less than 1.5%, or less than 1% of the batch to determine existenceof defects in the patterns processed onto the substrates.

According to an embodiment, the batch of substrates are processed usinga lithography apparatus.

Disclosed herein is a method of manufacturing a device comprising: thecomputer-implemented defect prediction method described above and atleast partially indicating which PWLPs to inspect based on thedetermined or predicted existence, probability of existence, one or morecharacteristics, or a combination thereof, of the defect.

According to an embodiment, the defect is one or more selected from:necking, line pull back, line thinning, CD error, overlapping, resisttop loss, resist undercut and/or bridging.

Disclosed herein is a defect determination or prediction method for alithographic process, wherein the method comprises a step of determiningor predicting existence, probability of existence, a characteristic, ora combination thereof, of a defect using a simulation of at least a partof the lithographic process.

According to an embodiment, the lithographic process comprises a devicemanufacturing process involving processing a pattern onto a substrate,the determined or predicted existence, probability of existence,characteristic, or combination thereof, of the defect being part of thepattern.

According to an embodiment, the defect is determined or predicted beforethe pattern is irreversibly processed onto the substrate.

According to an embodiment, the pattern is irreversibly processed ontothe substrate when the pattern is etched into at least part of thesubstrate, or when at least a part of the pattern is used for implantingions into the substrate.

According to an embodiment, the method comprises determining orpredicting existence, probability of existence, characteristic, orcombination thereof, of the defect for every substrate processed usingthe lithographic process.

According to an embodiment, a production parameter of a lithographicproduction tool is dependent on the step of determining or predictingexistence, probability of existence, characteristic, or combinationthereof, of the defect, the lithographic production tool beingconfigured for performing at least one step in the lithographic process.

Disclosed herein is a defect classification method for classifying adefect or a possible defect in a lithographic process, the methodcomprising a step of classifying the defect or the possible defect usinga simulation of at least a part of the lithographic process.

According to an embodiment, the lithographic process comprises a devicemanufacturing process involving processing a pattern onto a substrate.

Disclosed herein is a method of improving a capture rate of a defect ina lithographic process, the method comprising a step of determining orpredicting existence, probability of existence, characteristic, orcombination thereof, of the defect using a simulation of at least a partof the lithographic process.

According to an embodiment, the lithographic process comprises a devicemanufacturing process involving processing a pattern onto a substrate.

Disclosed herein is a method of selecting a pattern to be inspected froma plurality of patterns in a lithographic process, the method comprisinga step of selecting the pattern to be inspected at least partially basedon a simulation of at least a part of the lithographic process.

According to an embodiment, the lithographic process comprises a devicemanufacturing process involving processing the plurality of patternsonto a substrate.

According to an embodiment, the selected pattern is inspected to assesswhether the selected pattern is defective or whether a part of theselected pattern comprises a defect.

Disclosed herein is a method of defining an accuracy of a determinationor prediction of a defect in a lithographic process, the methodcomprising a step of defining an accuracy of a simulation of at least apart of the lithographic process, the simulation being used fordetermining or predicting an existence, probability of existence,characteristic, or combination thereof, of the defect.

According to an embodiment, the lithographic process comprises a devicemanufacturing process involving processing a pattern onto a substrate.

According to an embodiment, the accuracy of the determination orprediction of the defect is higher than an accuracy of a defectinspection tool used in the lithographic process.

Disclosed herein is a computer program product comprising a computerreadable medium having instructions recorded thereon, the instructionswhen executed by a computer implementing any of methods above.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of exampleonly, with reference to the accompanying schematic drawings in whichcorresponding reference symbols indicate corresponding parts, and inwhich:

FIG. 1 depicts a lithographic apparatus according to an embodiment ofthe invention;

FIG. 2 shows a flow chart for a method of determining existence ofdefects in a lithography process, according to an embodiment;

FIG. 3 shows exemplary sources of the processing parameters;

FIG. 4A shows an implementation of step 213 of FIG. 2;

FIG. 4B shows an alternative implementation of step 213 of FIG. 2.

FIG. 5A shows an exemplary substrate with many dies.

FIG. 5B shows a usable depth of focus (uDOF) obtained using atraditional method.

FIG. 5C shows a usable depth of focus (uDOF) obtained using a methodaccording to an embodiment described herein.

FIG. 6 shows a schematic flow diagram for a processing flow.

FIG. 7 shows an exemplary map for focus.

DETAILED DESCRIPTION

Although specific reference may be made in this text to the use oflithographic apparatus in the manufacture of ICs, it should beunderstood that the lithographic apparatus described herein may haveother applications, such as the manufacture of integrated opticalsystems, guidance and detection patterns for magnetic domain memories,liquid-crystal displays (LCDs), thin film magnetic heads, etc. Theskilled artisan will appreciate that, in the context of such alternativeapplications, any use of the terms “wafer” or “die” herein may beconsidered as synonymous with the more general terms “substrate” or“target portion”, respectively. The substrate referred to herein may beprocessed, before or after exposure, in for example a track (a tool thattypically applies a layer of resist to a substrate and develops theexposed resist) or a metrology or inspection tool. Where applicable, thedisclosure herein may be applied to such and other substrate processingtools. Further, the substrate may be processed more than once, forexample in order to create a multi-layer IC, so that the term substrateused herein may also refer to a substrate that already contains multipleprocessed layers.

The terms “radiation” and “beam” used herein encompass all types ofelectromagnetic radiation, including ultraviolet (UV) radiation (e.g.having a wavelength of 365, 248, 193, 157 or 126 nm) and extremeultra-violet (EUV) radiation (e.g. having a wavelength in the range of5-20 nm), as well as particle beams, such as ion beams or electronbeams.

The term “patterning device” used herein should be broadly interpretedas referring to a device that can be used to impart a radiation beamwith a pattern in its cross-section such as to create a pattern in atarget portion of the substrate. It should be noted that the patternimparted to the radiation beam may not exactly correspond to the desiredpattern in the target portion of the substrate. Generally, the patternimparted to the radiation beam will correspond to a particularfunctional layer in a device being created in the target portion, suchas an integrated circuit.

A patterning device may be transmissive or reflective. Examples ofpatterning device include masks, programmable mirror arrays, andprogrammable LCD panels. Masks are well known in lithography, andinclude mask types such as binary, alternating phase-shift, andattenuated phase-shift, as well as various hybrid mask types. An exampleof a programmable mirror array employs a matrix arrangement of smallmirrors, each of which can be individually tilted so as to reflect anincoming radiation beam in different directions; in this manner, thereflected beam is patterned.

The support structure holds the patterning device. It holds thepatterning device in a way depending on the orientation of thepatterning device, the design of the lithographic apparatus, and otherconditions, such as for example whether or not the patterning device isheld in a vacuum environment. The support can use mechanical clamping,vacuum, or other clamping techniques, for example electrostatic clampingunder vacuum conditions. The support structure may be a frame or atable, for example, which may be fixed or movable as required and whichmay ensure that the patterning device is at a desired position, forexample with respect to the projection system. Any use of the terms“reticle” or “mask” herein may be considered synonymous with the moregeneral term “patterning device”.

The term “projection system” used herein should be broadly interpretedas encompassing various types of projection system, including refractiveoptical systems, reflective optical systems, and catadioptric opticalsystems, as appropriate for example for the exposure radiation beingused, or for other factors such as the use of an immersion fluid or theuse of a vacuum. Any use of the term “projection lens” herein may beconsidered as synonymous with the more general term “projection system”.

The illumination system may also encompass various types of opticalcomponents, including refractive, reflective, and catadioptric opticalcomponents for directing, shaping, or controlling the beam of radiation,and such components may also be referred to below, collectively orsingularly, as a “lens”.

The lithographic apparatus may be of a type having two (dual stage) ormore substrate tables (and/or two or more support structures). In such“multiple stage” machines the additional tables may be used in parallel,or preparatory steps may be carried out on one or more tables while oneor more other tables are being used for exposure.

The lithographic apparatus may also be of a type wherein the substrateis immersed in a liquid having a relatively high refractive index, e.g.water, so as to fill a space between the final element of the projectionsystem and the substrate. Immersion techniques are well known in the artfor increasing the numerical aperture of projection systems.

FIG. 1 schematically depicts a lithographic apparatus according to aparticular embodiment of the invention. The apparatus comprises:

an illumination system (illuminator) IL to condition a beam PB ofradiation (e.g. UV radiation or DUV radiation).

a support structure MT to support a patterning device (e.g. a mask) MAand connected to first positioning device PM to accurately position thepatterning device with respect to item PL;

a substrate table (e.g. a wafer table) WT for holding a substrate (e.g.a resist coated wafer) W and connected to second positioning device PWfor accurately positioning the substrate with respect to item PL; and

a projection system (e.g. a refractive projection lens) PL configured toimage a pattern imparted to the radiation beam PB by patterning deviceMA onto a target portion C (e.g. comprising one or more dies) of thesubstrate W.

As here depicted, the apparatus is of a transmissive type (e.g.employing a transmissive mask). Alternatively, the apparatus may be of areflective type (e.g. employing a programmable mirror array of a type asreferred to above).

The illuminator IL receives a beam of radiation from a radiation sourceSO. The source and the lithographic apparatus may be separate entities,for example when the source is an excimer laser. In such cases, thesource is not considered to form part of the lithographic apparatus andthe radiation beam is passed from the source SO to the illuminator ILwith the aid of a beam delivery system BD comprising for examplesuitable directing mirrors and/or a beam expander. In other cases thesource may be an integral part of the apparatus, for example when thesource is a mercury lamp. The source SO and the illuminator IL, togetherwith the beam delivery system BD if required, may be referred to as aradiation system.

The illuminator IL may alter the intensity distribution of the beam. Theilluminator may be arranged to limit the radial extent of the radiationbeam such that the intensity distribution is non-zero within an annularregion in a pupil plane of the illuminator IL. Additionally oralternatively, the illuminator IL may be operable to limit thedistribution of the beam in the pupil plane such that the intensitydistribution is non-zero in a plurality of equally spaced sectors in thepupil plane. The intensity distribution of the radiation beam in a pupilplane of the illuminator IL may be referred to as an illumination mode.

The illuminator IL may comprise adjuster AM configured to adjust theintensity distribution of the beam. Generally, at least the outer and/orinner radial extent (commonly referred to as σ-outer and σ-inner,respectively) of the intensity distribution in a pupil plane of theilluminator can be adjusted. The illuminator IL may be operable to varythe angular distribution of the beam. For example, the illuminator maybe operable to alter the number, and angular extent, of sectors in thepupil plane wherein the intensity distribution is non-zero. By adjustingthe intensity distribution of the beam in the pupil plane of theilluminator, different illumination modes may be achieved. For example,by limiting the radial and angular extent of the intensity distributionin the pupil plane of the illuminator IL, the intensity distribution mayhave a multi-pole distribution such as, for example, a dipole,quadrupole or hexapole distribution. A desired illumination mode may beobtained, e.g., by inserting an optic which provides that illuminationmode into the illuminator IL or using a spatial light modulator.

The illuminator IL may be operable alter the polarization of the beamand may be operable to adjust the polarization using adjuster AM. Thepolarization state of the radiation beam across a pupil plane of theilluminator IL may be referred to as a polarization mode. The use ofdifferent polarization modes may allow greater contrast to be achievedin the image formed on the substrate W. The radiation beam may beunpolarized. Alternatively, the illuminator may be arranged to linearlypolarize the radiation beam. The polarization direction of the radiationbeam may vary across a pupil plane of the illuminator IL. Thepolarization direction of radiation may be different in differentregions in the pupil plane of the illuminator IL. The polarization stateof the radiation may be chosen in dependence on the illumination mode.For multi-pole illumination modes, the polarization of each pole of theradiation beam may be generally perpendicular to the position vector ofthat pole in the pupil plane of the illuminator IL. For example, for adipole illumination mode, the radiation may be linearly polarized in adirection that is substantially perpendicular to a line that bisects thetwo opposing sectors of the dipole. The radiation beam may be polarizedin one of two different orthogonal directions, which may be referred toas X-polarized and Y-polarized states. For a quadrupole illuminationmode the radiation in the sector of each pole may be linearly polarizedin a direction that is substantially perpendicular to a line thatbisects that sector. This polarization mode may be referred to as XYpolarization. Similarly, for a hexapole illumination mode the radiationin the sector of each pole may be linearly polarized in a direction thatis substantially perpendicular to a line that bisects that sector. Thispolarization mode may be referred to as TE polarization.

In addition, the illuminator IL generally comprises various othercomponents, such as an integrator IN and a condenser CO. The illuminatorprovides a conditioned beam of radiation PB, having a desired uniformityand intensity distribution in its cross section.

The radiation beam PB is incident on the patterning device (e.g. mask)MA, which is held on the support structure MT. Having traversed thepatterning device MA, the beam PB passes through the lens PL, whichfocuses the beam onto a target portion C of the substrate W. With theaid of the second positioning device PW and position sensor IF (e.g. aninterferometric device), the substrate table WT can be moved accurately,e.g. so as to position different target portions C in the path of thebeam PB. Similarly, the first positioning device PM and another positionsensor (which is not explicitly depicted in FIG. 1) can be used toaccurately position the patterning device MA with respect to the path ofthe beam PB, e.g. after mechanical retrieval from a mask library, orduring a scan. In general, movement of the object tables MT and WT willbe realized with the aid of a long-stroke module (coarse positioning)and a short-stroke module (fine positioning), which form part of thepositioning device PM and PW. However, in the case of a stepper (asopposed to a scanner) the support structure MT may be connected to ashort stroke actuator only, or may be fixed. Patterning device MA andsubstrate W may be aligned using patterning device alignment marks M1,M2 and substrate alignment marks P1, P2.

The depicted apparatus can be used in the following preferred modes:

1. In step mode, the support structure MT and the substrate table WT arekept essentially stationary, while an entire pattern imparted to thebeam PB is projected onto a target portion C in one go (i.e. a singlestatic exposure). The substrate table WT is then shifted in the X and/orY direction so that a different target portion C can be exposed. In stepmode, the maximum size of the exposure field limits the size of thetarget portion C imaged in a single static exposure.2. In scan mode, the support structure MT and the substrate table WT arescanned synchronously while a pattern imparted to the beam PB isprojected onto a target portion C (i.e. a single dynamic exposure). Thevelocity and direction of the substrate table WT relative to the supportstructure MT is determined by the (de-)magnification and image reversalcharacteristics of the projection system PL. In scan mode, the maximumsize of the exposure field limits the width (in the non-scanningdirection) of the target portion in a single dynamic exposure, whereasthe length of the scanning motion determines the height (in the scanningdirection) of the target portion.3. In another mode, the support structure MT is kept essentiallystationary holding a programmable patterning device, and the substratetable WT is moved or scanned while a pattern imparted to the beam PB isprojected onto a target portion C. In this mode, generally a pulsedradiation source is employed and the programmable patterning device isupdated as required after each movement of the substrate table WT or inbetween successive radiation pulses during a scan. This mode ofoperation can be readily applied to maskless lithography that utilizesprogrammable patterning device, such as a programmable mirror array of atype as referred to above.

Combinations and/or variations on the above described modes of use orentirely different modes of use may also be employed.

The projection system PL has an optical transfer function which may benon-uniform, which can affect the pattern imaged on the substrate W. Forunpolarized radiation such effects can be fairly well described by twoscalar maps, which describe the transmission (apodization) and relativephase (aberration) of radiation exiting the projection system PL as afunction of position in a pupil plane thereof. These scalar maps, whichmay be referred to as the transmission map and the relative phase map,may be expressed as a linear combination of a complete set of basicfunctions. A particularly convenient set is the Zernike polynomials,which form a set of orthogonal polynomials defined on a unit circle. Adetermination of each scalar map may involve determining thecoefficients in such an expansion. Since the Zernike polynomials areorthogonal on the unit circle, the Zernike coefficients may bedetermined by calculating the inner product of a measured scalar mapwith each Zernike polynomial in turn and dividing this by the square ofthe norm of that Zernike polynomial.

The transmission map and the relative phase map are field and systemdependent. That is, in general, each projection system PL will have adifferent Zernike expansion for each field point (i.e. for each spatiallocation in its image plane). The relative phase of the projectionsystem PL in its pupil plane may be determined by projecting radiation,for example from a point-like source in an object plane of theprojection system PL (i.e. the plane of the patterning device MA),through the projection system PL and using a shearing interferometer tomeasure a wavefront (i.e. a locus of points with the same phase). Ashearing interferometer is a common path interferometer and therefore,advantageously, no secondary reference beam is required to measure thewavefront. The shearing interferometer may comprise a diffractiongrating, for example a two dimensional grid, in an image plane of theprojection system (i.e. the substrate table WT) and a detector arrangedto detect an interference pattern in a plane that is conjugate to apupil plane of the projection system PL. The interference pattern isrelated to the derivative of the phase of the radiation with respect toa coordinate in the pupil plane in the shearing direction. The detectormay comprise an array of sensing elements such as, for example, chargecoupled devices (CCDs).

The diffraction grating may be sequentially scanned in two perpendiculardirections, which may coincide with axes of a co-ordinate system of theprojection system PL (x and y) or may be at an angle such as 45 degreesto these axes. Scanning may be performed over an integer number ofgrating periods, for example one grating period. The scanning averagesout phase variation in one direction, allowing phase variation in theother direction to be reconstructed. This allows the wavefront to bedetermined as a function of both directions.

The projection system PL of a state of the art lithographic apparatus LAmay not produce visible fringes and therefore the accuracy of thedetermination of the wavefront can be enhanced using phase steppingtechniques such as, for example, moving the diffraction grating.Stepping may be performed in the plane of the diffraction grating and ina direction perpendicular to the scanning direction of the measurement.The stepping range may be one grating period, and at least three(uniformly distributed) phase steps may be used. Thus, for example,three scanning measurements may be performed in the y-direction, eachscanning measurement being performed for a different position in thex-direction. This stepping of the diffraction grating effectivelytransforms phase variations into intensity variations, allowing phaseinformation to be determined. The grating may be stepped in a directionperpendicular to the diffraction grating (z direction) to calibrate thedetector.

The transmission (apodization) of the projection system PL in its pupilplane may be determined by projecting radiation, for example from apoint-like source in an object plane of the projection system PL (i.e.the plane of the patterning device MA), through the projection system PLand measuring the intensity of radiation in a plane that is conjugate toa pupil plane of the projection system PL, using a detector. The samedetector as is used to measure the wavefront to determine aberrationsmay be used. The projection system PL may comprise a plurality ofoptical (e.g., lens) elements and may further comprise an adjustmentmechanism PA configured to adjust one or more of the optical elements soas to correct for aberrations (phase variations across the pupil planethroughout the field). To achieve this, the adjustment mechanism PA maybe operable to manipulate one or more optical (e.g., lens) elementswithin the projection system PL in one or more different ways. Theprojection system may have a co-ordinate system wherein its optical axisextends in the z direction. The adjustment mechanism PA may be operableto do any combination of the following: displace one or more opticalelements; tilt one or more optical elements; and/or deform one or moreoptical elements. Displacement of optical elements may be in anydirection (x, y, z or a combination thereof). Tilting of opticalelements is typically out of a plane perpendicular to the optical axis,by rotating about axes in the x or y directions although a rotationabout the z axis may be used for non-rotationally symmetric asphericaloptical elements. Deformation of optical elements may include both lowfrequency shapes (e.g. astigmatic) and high frequency shapes (e.g. freeform aspheres). Deformation of an optical element may be performed forexample by using one or more actuators to exert force on one or moresides of the optical element and/or by using one or more heatingelements to heat one or more selected regions of the optical element. Ingeneral, it may not be possible to adjust the projection system PL tocorrect for apodizations (transmission variation across the pupilplane). The transmission map of a projection system PL may be used whendesigning a patterning device (e.g., mask) MA for the lithographicapparatus LA. Using a computational lithography technique, thepatterning device MA may be designed to at least partially correct forapodizations.

Various patterns on a patterning device may have different processwindows (i.e., a space of processing parameters under which a patternwill be produced within specifications). Examples of patternspecifications that relate to potential systematic defects includechecks for necking, line pull back, line thinning, CD, edge placement,overlapping, resist top loss, resist undercut and bridging. The processwindow of all the patterns on a patterning device may be obtained bymerging (e.g., overlapping) process windows of each individual pattern.The boundary of the process window of all the patterns containsboundaries of process windows of some of the individual patterns. Inanother word, these individual patterns limit the process window of allthe patterns. These patterns can be referred to as “hot spots” or“process window limiting patterns (PWLPs),” which are usedinterchangeably herein. When controlling a lithography process, it ispossible and economical to focus on the hot spots. When the hot spotsare not defective, it is most likely that the all the patterns are notdefective.

Processing parameters may vary with position on a substrate and withtime (e.g., between substrates, between dies). Such variation may becaused by change in the environment such as temperature and humidity.Other causes of such variations may include drift in one or morecomponents in the processing apparatus such as the source, projectionoptics, substrate table, height variations of substrate surfaces, etc.in a lithography apparatus. It would be beneficial to be aware of suchvariations and their effects on PWLPs or potential patterning defects,and to adjust the lithography process to accommodate such variations soas to reduce actual defects. To reduce the computational cost of trackthese variations, one can again monitor only the hot spots.

FIG. 2 shows a flow chart for a method of determining existence ofdefects in a lithography process, according to an embodiment. In step211, hot spots or locations thereof are identified using any suitablemethod from patterns (e.g., patterns on a patterning device). Forexample, hot spots may be identified by analyzing patterns on patternsusing an empirical model or a computational model. In an empiricalmodel, images (e.g., resist image, optical image, etch image) of thepatterns are not simulated; instead, the empirical model predictsdefects or probability of defects based on correlations betweenprocessing parameters, parameters of the patterns, and the defects. Forexample, an empirical model may be a classification model or a databaseof patterns prone to defects. In a computational model, a portion or acharacteristic of the images is calculated or simulated, and defects areidentified based on the portion or the characteristic. For example, aline pull back defect may be identified by finding a line end too faraway from its desired location; a bridging defect may be identified byfinding a location where two lines undesirably join; an overlappingdefect may be identified by finding two features on separate layersundesirably overlap or undesirably not overlap. An empirical model isusually less computationally expensive than a computational model. It ispossible to determine and/or compile process windows of the hot spotsinto a map, based on hotspot locations and process windows of individualhot spots—i.e. determine process windows as a function of location. Thisprocess window map may characterize the layout-specific sensitivitiesand processing margins of the patterns. In another example, the hotspots, their locations, and/or their process windows may be determinedexperimentally, such as by FEM wafer inspection or a suitable metrologytool. The defects may include those defects that cannot be detected inan after-development-inspection (ADI) (usually optical inspection), suchas resist top loss, resist undercut, etc. Conventional inspection onlyreveals such defects after the substrate is irreversibly processed(e.g., etched, ion implanted), at which point the wafer cannot bereworked. So, such resist top loss defects cannot be detected using thecurrent optical technology at the time of drafting this document.However, simulation may be used to determine where resist top loss mayoccur and what the severity would be. Based on this information, it maybe either decided to inspect the specific possible defect using a moreaccurate inspection method (and typically more time consuming) todetermine whether the defect needs rework, or it may be decided torework the imaging of the specific resist layer (remove the resist layerhaving the resist top loss defect and recoat the wafer to redo theimaging of the specific layer) before the irreversible processing (e.g.,etching) is done.

In step 212, processing parameters under which the hot spots areprocessed (e.g., imaged or etched onto a substrate) are determined. Theprocessing parameters may be local dependent on the locations of the hotspots, the dies, or both. The processing parameters may beglobal—independent of the locations of the hot spots and the dies. Oneexemplary way to determine the processing parameters is to determine thestatus of the lithographic apparatus. For example, laser bandwidth,focus, dose, source parameters, projection optics parameters, and thespatial or temporal variations of these parameters, may be measured fromthe lithographic apparatus. Another exemplary way is to infer theprocessing parameters from data obtained from metrology performed on thesubstrate, or from operator of the processing apparatus. For example,metrology may include inspecting a substrate using a diffractive tool(e.g., ASML YieldStar), an electron microscope, or other suitableinspection tools. It is possible to obtain processing parameters for anylocation on a processed substrate, including the identified hot spots.The processing parameters may be compiled into a map—lithographicparameters, or process conditions, as a function of location. FIG. 7shows an exemplary map for focus. Of course, other processing parametersmay be represented as functions of location, i.e., a map. In anembodiment, the processing parameters may be determined before, andpreferably immediately before processing each hotspot. In an alternativeembodiment such map comprises processing parameters which may beconstituted from different data sources. For example, for estimatingfocus errors, we may combine wafer metrology data from a metrologysystem (for example, a diffraction based metrology system such as theASML YieldStar) with data from the lithographic exposure tool, forexample, from a levelling system of the lithographic exposure tool whichis used to level an exposure surface underneath the exposure optics ofthe lithographic exposure tool before exposing a radiation sensitivelayer on a substrate. One of the different data sources may, forexample, include a relatively high data density, while another datasource may, for example, include fewer data points but more accuratedata values. Combining these two different data sources enables thegeneration of a processing parameter map in which the relatively highdensity data is also relatively accurate due to the calibration to thefewer, more accurate data points of the other data source. Suchprocessing parameter map may, for example, be generated by dividing thefull image field of a lithographic tool into sub-areas, for example ofapproximately 1×1 mm size and determine the processing parameter mapfrom pattern analysis in these sub-areas—for example, determining aDepth-of-Focus map, a Dose-latitude map, a focus map or a dose offsetmap. Next, the processing parameter value in each sub-area is assigned anumber such that each pixel in the sub-area comprises a value of theprocessing parameter (the pixel size depending on the data densityinside the sub-area. Even further alternatively, such processingparameter map may even be generated for a specific reticle or maskcomprising the pattern and used to transfer the pattern onto thesubstrate using the lithographic tool. This would result in a processingparameter map specifically for a specific reticle. Subsequently using,for example, simulations using a model of a specific lithographicexposure tool may even allow to include a characteristic signature ofthe lithographic exposure tool into the processing parameter map suchthat the processing parameter map may even become reticle and exposuretool specific. Performing such simulations for multiple tools may evenallow a user to select the best lithographic tool for multiplelithographic tools for imaging a specific reticle—of course not takingany temporal drifts of the lithographic exposure tool into account. Sucha exposure tool specific map may also be used to allow for adjustmentsof other processing parameter values than the ones mapped in theprocessing parameter map to ensure that PWLPs which may need to beimaged at non-favourable processing parameters values still get imagedwithin specification. For example, when a specific PWLP may not beimaged correctly in focus (which may have an impact on the criticaldimension of the PWLP), an other processing parameter value such as dosemay be adapted—possibly locally—to ensure that the overall dimension ofthe PWLP is still within specifications. Finally, each of the abovedescribed processing parameter maps may, for example, be converted intoa kind of constraint map. Such a constraint map may, for example,indicate within which range the processing parameters at a certainlocation may vary without endangering the PWLPs. Alternatively, theconstraint map may, for example, comprise a weight map indicating whichareas of the design require the processing parameters to be close to theoptimal parameter setting and what areas of the design allow a largerrange of processing parameter values.

In step 213, existence, probability of existence, characteristics, or acombination thereof, of a defect at a hot spot is determined using theprocessing parameters under which the hot spot is processed. Thisdetermination may be simply comparing the processing parameters and theprocess window of the hot spot—if the processing parameters fall withinthe process window, no defect exists; if the processing parameters falloutside the process window, at least one defect will be expected toexist. This determination may also be done using a suitable empiricalmodel (including a statistical model). For example, a classificationmodel may be used to provide a probability of existence of a defect.Another way to make this determination is to use a computational modelto simulate an image or expected patterning contours of the hot spotunder the processing parameters and measure the image or contourparameters. In an embodiment, the processing parameters may bedetermined immediately (i.e., before processing the pattern or the nextsubstrate) after processing a pattern or a substrate. The determinedexistence and/or characteristics of a defect may serve as a basis for adecision of disposition: rework or acceptance. In an embodiment, theprocessing parameters may be used to calculate moving averages of thelithographic parameters. Moving averages are useful to capture long termdrifts of the lithographic parameters, without distraction by short termfluctuations.

In optional step 214, the processing parameters may be adjusted usingthe existence, probability of existence, characteristics, or acombination thereof as determined in step 213 (i.e., the prediction ordetermination is fed back to adjust the processing parameters), so thatthe defect is eliminated or its severity reduced. For example, if a hotspot is located on a bump of a substrate, changing the focus mayeliminate the defect on that hot spot. Preferably, the processingparameters are adjusted immediately before processing each hot spot.Steps 213 and 214 may be reiterative. Processing parameters may also beadjusted after processing of one or multiple substrates, especially whenan average (e.g., a moving average) of the processing parameters aredetermined, in order to compensate for systematic or slowly varyingprocess variations, or to address a larger number of adjustableprocessing parameters. Adjustment of processing parameters may include,focus, dose, source or pupil phase adjustments.

In optional step 215, existence and/or characteristics of a residuedefect may be determined using the adjusted processing parameters. Aresidue defect is a defect that cannot be eliminated by adjusting theprocessing parameters. This determination may be simply comparing theadjusted processing parameters and the process window of the hot spot—ifthe processing parameters fall within the process window, no residuedefect is expected to exist; if the processing parameters fall outsidethe process window, at least one residue defect will be expected toexist. This determination may also be done using a suitable empiricalmodel (including a statistical model). For example, a classificationmodel may be used to provide a probability of existence of a residuedefect. Another way to make this determination is to use a computationalmodel to simulate an image or expected patterning contours of the hotspot under the adjusted processing parameters and measure the image orcontour parameters. The determined existence and/or characteristics of aresidue defect may serve as a basis for a decision of disposition:rework or acceptance.

Optionally, an indication which hot spots are subject to inspection maybe made at least partially based on the determined or predictedexistence, probability of existence, one or more characteristics, or acombination thereof, of the residue defect. For example, if a substratehas a probability of having one or more residue defects, the substratemay be subject to substrate inspection. The prediction or determinationof residue defects feeds forward to inspection.

FIG. 3 shows exemplary sources of the processing parameters 350. Onesource may be data 310 of the processing apparatus, such as parametersof the source, projection optics, substrate stage, etc. of a lithographyapparatus. Another source may be data 320 from various substratemetrology tools, such as wafer height map, focus map, CDU map, etc. Data320 may be obtained before substrates were subject to a step (e.g.,etch) that prevents reworking of the substrate. Another source may bedata 330 from various patterning device metrology tools, mask CDU map,mask film stack parameters variation, etc. Yet another source may bedata 340 from an operator of the processing apparatus.

FIG. 4A shows an implementation of step 213 of FIG. 2. In step 411, theprocess window of a hot spot is obtained, either by using a model or byquerying a database. For example, the process window may be a spacespanned by processing parameters such as focus and dose. In step 412,processing parameters determined in step 212 of FIG. 2 is compared withthe process window. If the processing parameters fall within the processwindow, no defect exists; if the processing parameters fall outside theprocess window, at least one defect is expected to exist.

FIG. 4B shows an alternative implementation of step 213 of FIG. 2. Theprocessing parameters 420 may be used as input (e.g., independentvariables) to a classification model 430. The processing parameters 420may include characteristics of the source (e.g., intensity, pupilprofile, etc.), characteristics of the projection optics, dose, focus,characteristics of the resist, characteristics of development andpost-exposure baking of the resist, and characteristics of etching. Theterm “classifier” or “classification model” sometimes also refers to themathematical function, implemented by a classification algorithm, thatmaps input data to a category. In machine learning and statistics,classification is the problem of identifying to which of a set ofcategories 440 (sub-populations) a new observation belongs, on the basisof a training set of data containing observations (or instances) whosecategory membership is known. The individual observations are analyzedinto a set of quantifiable properties, known as various explanatoryvariables, features, etc. These properties may variously be categorical(e.g. “good”—a lithographic process that does not produce defects or“bad”—a lithographic process that produces defects; “type 1”, “type 2”,. . . “type n”—different types of defects). Classification is consideredan instance of supervised learning, i.e. learning where a training setof correctly identified observations is available. Examples ofclassification models are, logistic regression and multinomial logit,probit regression, the perceptron algorithm, support vector machines,import vector machines, and linear discriminant analysis.

One example of the processing parameters is substrate levelling. FIG. 5Ashows an exemplary substrate with many dies (depicted as grids). In adie called out, hot spots (depicted as circles) are identified alongwith less critical positions (i.e., positions that are not processwindow limiting, depicted as diamonds) in the patterns in the die. FIG.5B shows a usable depth of focus (uDOF) obtained using a traditionalmethod. uDOF is the depth of focus that falls within the process windowsof all the patterns in an exposure slit. FIG. 5C shows a usable depth offocus (uDOF) obtained using a method according to an embodimentdescribed herein, where less critical positions regions (diamonds) areallowed to drift farther away from their respective best focuses tobring the best focuses of the hot spots (circles) closer by adjustingthe processing parameters including the substrate levelling, therebyincreasing the uDOF. According to an embodiment, a method describedherein allows adjustment of processing parameters for each substrate oreven each die. FIG. 6 shows a schematic flow diagram for a processingflow. In step 610, processing parameters immediately (e.g., afterprocessing the immediately previous substrate or die) before processinga substrate or a die are determined. In step 620, a prediction ordetermination of the existence of a defect, probability of existence ofa defect, a characteristic of a defect, or a combination thereof is madeusing the processing parameters immediately before processing thesubstrate or the die, and using a characteristic of the substrate or thedie (e.g., as determined from metrology on the substrate or the die)and/or a characteristic of the geometry of patterns to be processed ontothe substrate or the die. In step 630, the processing parameters areadjusted based on the prediction so as to eliminate, reduce theprobability or severity of the defect. Alternatively, it may be knownfrom simulations of the layout to be processed that the PWLP may belocated at a specific area within a die. In such a situation, the systemin the imaging tool which ensures the leveling of the die beforeexposure in the imaging tool may ensure that this specific area is infocus allowing other areas of the die to divert further from focus toensure that the PWLP are imaged in spec. The simulations may further beused to determine whether the less critical structures are still imagedcorrectly due to the less favourable processing conditions because ofthe preferred leveling accuracy of the area containing the PWLPs.Simulations may also be used to ensure that all types of PWLPs areactually found in the design and that the location of all PWLPs isactually known and preferably put in a PWLP-map. Furthermore, a searchalgorithm may be applied across a chip design to find, for example,PWLPs which are known and which may, for example, be listed in a kind of“hot-spot database”. Although probably somewhat less accurate, suchsearch algorithm may be faster than simulating the full chip design andmay be used to relatively quickly find known PWLPs. According to anembodiment, a method described herein allows inspection of lesssubstrates among a production batch while maintaining comparable defectrates to that in a conventional processing flow. A conventionalprocessing flow involves processing (e.g., exposing in a lithographyapparatus) a batch of substrates, 2%-3% or more of the batch has to beinspected in order to catch most of the defects. By using the defectprediction method according to the current embodiments, the availablemetrology data is used to virtually inspect wafers and predict possibledefects on these wafers. Because the defect prediction method accordingto the embodiments is virtual, substantially every wafer produced in thelithographic process may be ‘virtually’ inspected and thus achievesubstantially 100% inspection coverage. This extensive ‘virtual’inspection also provides more feedback data which enables a moreaccurate and quicker corrective action look which typically reduces anydrift in the lithographic exposure tools.

The invention may further be described using the following clauses:

1. A computer-implemented defect determination or prediction method fora device manufacturing process involving processing a pattern onto asubstrate, the method comprising:

identifying a processing window limiting pattern (PWLP) from thepattern;

determining a processing parameter under which the PWLP is processed;and

determining or predicting, using the processing parameter, existence,probability of existence, a characteristic, or a combination thereof, ofa defect produced from the PWLP with the device manufacturing process.

2. The method of clause 1, wherein the determining or predicting theexistence, the probability of existence, the characteristic, or thecombination thereof, further uses a characteristic of the PWLP, acharacteristic of the pattern, or both.3. The method of clause 1 or clause 2, further comprising adjusting theprocessing parameter using the existence, the probability of existence,the characteristic, or the combination thereof, of the defect.4. The method of clause 3, further comprising carrying out reiterativelythe determining or predicting the existence, the probability ofexistence, the characteristic, or the combination thereof, of thedefect, and adjusting the processing parameter.5. The method of clause 3 or clause 4, further comprising determining orpredicting, using the adjusted processing parameter, existence,probability of existence, a characteristic, or a combination thereof, ofa residue defect produced from the PWLP using the device manufacturingprocess.6. The method of clause 5, further comprising indicating which of aplurality of PWLPs to inspect at least partially based on the determinedor predicted existence, probability of existence, the characteristic, orthe combination thereof, of the residue defect.7. The method of any of clauses 1 to 6, further comprising determining aprocess window of the PWLP.8. The method of clause 7, wherein the determining or predicting theexistence, the probability of existence, the characteristic, or thecombination thereof, of the defect comprises comparing the processingparameter with the process window.9. The method any of clauses 1 to 8, further comprising compiling theprocessing parameter into a processing parameters map.10. The method of any of clauses 1 to 9, wherein the PWLP is identifiedusing an empirical model or a computational model.11. The method of any of clauses 1 to 10, wherein the processingparameter is any one or more selected from: focus, dose, a sourceparameter, a projection optics parameter, data obtained from metrology,and/or data from an operator of a processing apparatus used in thedevice manufacturing process.12. The method of clause 11, wherein the processing parameter is dataobtained from metrology and the data obtained from metrology is obtainedfrom a diffractive tool, or an electron microscope.13. The method of any of clauses 1 to 12, wherein the processingparameter is determined or predicted using a model or by querying adatabase.14. The method of any of clauses 1 to 13, wherein the determining orpredicting the existence, the probability of existence, thecharacteristic, or the combination thereof, of the defect comprisesusing a classification model with the processing parameter as input tothe classification model.15. The method of clause 14, wherein the classification model isselected from a group consisting of logistic regression and multinomiallogit, probit regression, the perceptron algorithm, support vectormachine, import vector machine, and linear discriminant analysis.16. The method of any of clauses 1 to 12, wherein the determining orpredicting the existence, the probability of existence, thecharacteristic, or the combination thereof, of the defect comprisessimulating an image or expected patterning contours of the PWLP underthe processing parameter and determining an image or contour parameter.17. The method of any of clauses 1 to 16, wherein the devicemanufacturing process involves using a lithography apparatus.18. The method of any of clauses 1 to 17, wherein the processingparameter is determined immediately before the PWLP is processed.19. The method of any of clauses 1 to 18, wherein the processingparameter is selected from local processing parameters or globalprocessing parameters.20. The method of any of clauses 1 to 19, wherein identifying the PWLPincludes identifying a location thereof21. The method of any of clauses 1 to 20, wherein the defect isundetectable before the substrate is irreversibly processed.22. A method of manufacturing a device involving processing a patternonto a substrate or onto a die of the substrate, the method comprising:

determining a processing parameter before processing the substrate orthe die;

predicting or determining existence of a defect, probability ofexistence of a defect, a characteristic of a defect, or a combinationthereof, using the processing parameter before processing the substrateor the die, and using a characteristic of the substrate or the die, acharacteristic of the geometry of a pattern to be processed onto thesubstrate or the die, or both;

adjusting the processing parameter based on the prediction ordetermination so as to eliminate, reduce the probability of or reducethe severity of, the defect.

23. The method of clause 22, further comprising identifying a processingwindow limiting pattern (PWLP) from the pattern.24. The method of clause 23, wherein the defect is a defect producedfrom the PWLP.25. The method of clause 23, wherein the characteristic of the substrateor the die is a process window of the PWLP.26. A method of manufacturing a device involving processing a patternonto a batch of substrates, the method including: processing the batchof substrates, and destructively inspecting less than 2%, less than1.5%, or less than 1% of the batch to determine existence of a defect inthe pattern processed onto the substrates.27. The method of clause 26, wherein the batch of substrates areprocessed using a lithography apparatus.28. A method of manufacturing a device comprising:

the computer-implemented defect prediction method according to any ofclauses 1 to 27; and

indicating which of plurality of PWLPs to inspect at least partiallybased on the determined or predicted existence, probability ofexistence, characteristic, or the combination thereof, of the defect.

29. The method of any of clauses 1 to 28, wherein the defect is one ormore selected from: necking, line pull back, line thinning, CD error,overlapping, resist top loss, resist undercut and/or bridging.30. A computer program product comprising a computer readable mediumhaving instructions recorded thereon, the instructions when executed bya computer implementing the method of any of clauses 1 to 29.31. A defect determination or prediction method for a lithographicprocess, wherein the method comprises a step of determining orpredicting existence, probability of existence, a characteristic, or acombination thereof, of a defect using a simulation of at least a partof the lithographic process.32. The defect determination or prediction method according to clause31, wherein the lithographic process comprises a device manufacturingprocess involving processing a pattern onto a substrate, the determinedor predicted existence, probability of existence, characteristic, orcombination thereof, of the defect being part of the pattern.33. The defect determination or prediction method according to clause32, wherein the defect is determined or predicted before the pattern isirreversibly processed onto the substrate.34. The defect determination or prediction method according to clause33, wherein the pattern is irreversibly processed onto the substratewhen the pattern is etched into at least part of the substrate, or whenat least a part of the pattern is used for implanting ions into thesubstrate.35. The defect determination or prediction method according to any ofthe clauses 31 to 34, wherein the method comprises determining orpredicting existence, probability of existence, characteristic, orcombination thereof, of the defect for every substrate processed usingthe lithographic process.36. The defect determination or prediction method according to any ofthe clauses 31 to 35, wherein a production parameter of a lithographicproduction tool is dependent on the step of determining or predictingexistence, probability of existence, characteristic, or combinationthereof, of the defect, the lithographic production tool beingconfigured for performing at least one step in the lithographic process.37. A defect classification method for classifying a defect or apossible defect in a lithographic process, the method comprising a stepof classifying the defect or the possible defect using a simulation ofat least a part of the lithographic process.38. The defect classification method according to clause 37, wherein thelithographic process comprises a device manufacturing process involvingprocessing a pattern onto a substrate.39. A method of improving a capture rate of a defect in a lithographicprocess, the method comprising a step of determining or predictingexistence, probability of existence, characteristic, or combinationthereof, of the defect using a simulation of at least a part of thelithographic process.40. The method according to clause 39, wherein the lithographic processcomprises a device manufacturing process involving processing a patternonto a substrate.41. A method of selecting a pattern to be inspected from a plurality ofpatterns in a lithographic process, the method comprising a step ofselecting the pattern to be inspected at least partially based on asimulation of at least a part of the lithographic process.42. The method according to clause 41, wherein the lithographic processcomprises a device manufacturing process involving processing theplurality of patterns onto a substrate.43. The method according to any of the clauses 41 or 42, wherein theselected pattern is inspected to assess whether the selected pattern isdefective or whether a part of the selected pattern comprises a defect.44. A method of defining an accuracy of a determination or prediction ofa defect in a lithographic process, the method comprising a step ofdefining an accuracy of a simulation of at least a part of thelithographic process, the simulation being used for determining orpredicting an existence, probability of existence, characteristic, orcombination thereof, of the defect.45. The method according to clause 44, wherein the lithographic processcomprises a device manufacturing process involving processing a patternonto a substrate.46. The method according to any of the clauses 44 or 45, wherein theaccuracy of the determination or prediction of the defect is higher thanan accuracy of a defect inspection tool used in the lithographicprocess.47. A computer program product comprising a computer readable mediumhaving instructions recorded thereon, the instructions when executed bya computer implementing the method of any of clauses 31 to 46.48. The computer readable medium of clause 47, wherein the machineexecutable instructions further comprise instructions for activating atleast some of the method steps using a connection to the computerreadable medium from a remote computer.49. The computer readable medium of clause 48, wherein the connectionwith the remote computer is a secured connection.50. The computer readable medium of any of the clauses 48 and 49,wherein the processing parameter is provided by the remote computer.51. The computer readable medium of clause 50, wherein the method isfurther configured for providing the determination or prediction, usingthe processing parameter, of the existence, probability of existence, acharacteristic, or a combination thereof, of a defect produced with thedevice manufacturing process back to the remote computer.52 A defect inspection system configured for inspecting the processingwindow limiting pattern determined or predicted using the method of anyof the clauses 1 to 46 or using the computer readable medium of any ofthe clauses 47 to 51.53. The defect inspection system of clause 52, wherein the remotecomputer is part of the defect inspection system.54. A substrate comprising the processing window limiting pattern (PWLP)and further comprising a metrology target for determining the processingparameter under which the processing window limiting pattern isprocessed for determining or predicting existence, probability ofexistence, a characteristic, or a combination thereof, of a defectproduced from the PWLP with the device manufacturing process accordingto the method of any of the clauses 1 to46 or according to the computer readable medium of any of the clauses 47to 51.55. The substrate according to clause 54, wherein the substrate is awafer comprising at least some of the layers of an integrated circuit.56. A lithographic imaging apparatus configured for imaging theprocessing window limiting pattern and further configured fordetermining the processing parameter under which the processing windowlimiting pattern is processed.57. The lithographic imaging apparatus according to clause 56, whereinthe lithographic imaging apparatus comprises the remote computer forproviding the processing parameter to the computer readable mediumaccording to clause 50.58. A database comprising the processing parameter for use in the methodof any of the clauses 1 to 46 or for use in the computer readable mediumof any of the clauses 47 to 51.59. The database according to clause 58, wherein the database furthercomprises the processing window limiting pattern associated with theprocessing parameters.60. A data carrier comprising the database according to any of theclauses 58 and 59.

Embodiments of the invention may be implemented in hardware, firmware,software, or any combination thereof. Embodiments of the invention mayalso be implemented as instructions stored on a machine-readable medium,which may be read and executed by one or more processors. Amachine-readable medium may include any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputing device). For example, a machine-readable medium may includeread only memory (ROM); random access memory (RAM); magnetic diskstorage media; optical storage media; flash memory devices; electrical,optical, acoustical or other forms of propagated signals (e.g. carrierwaves, infrared signals, digital signals, etc.), and others. Further,firmware, software, routines, instructions may be described herein asperforming certain actions. However, it should be appreciated that suchdescriptions are merely for convenience and that such actions in factresult from computing devices, processors, controllers, or other devicesexecuting the firmware, software, routines, instructions, etc.

While specific embodiments of the invention have been described above,it will be appreciated that the invention may be practiced otherwisethan as described. The description is not intended to limit theinvention.

1. A computer-implemented defect determination or prediction method fora device manufacturing process involving processing a pattern onto asubstrate, the method comprising: identifying a processing windowlimiting pattern (PWLP) from the pattern; determining a processingparameter under which the processing window limiting pattern isprocessed; and determining or predicting, using the processing parameterand the computer, existence, probability of existence, a characteristic,or a combination thereof, of a defect produced from the processingwindow limiting pattern with the device manufacturing process.
 2. Themethod of claim 1, wherein the determining or predicting the existence,the probability of existence, the characteristic, or the combinationthereof, further uses a characteristic of the processing window limitingpattern, a characteristic of the pattern, or both.
 3. The method ofclaim 1, further comprising adjusting the processing parameter using theexistence, the probability of existence, the characteristic, or thecombination thereof, of the defect.
 4. The method of claim 3, furthercomprising determining or predicting, using the adjusted processingparameter, existence, probability of existence, a characteristic, or acombination thereof, of a residue defect produced from the processingwindow limiting pattern using the device manufacturing process.
 5. Themethod of claim 4, further comprising indicating which of a plurality ofprocessing window limiting patterns to inspect at least partially basedon the determined or predicted existence, probability of existence, thecharacteristic, or the combination thereof, of the residue defect. 6.The method of claim 1, further comprising determining a process windowof the processing window limiting pattern.
 7. The method of claim 6,wherein the determining or predicting the existence, the probability ofexistence, the characteristic, or the combination thereof, of the defectcomprises comparing the processing parameter with the process window. 8.The method of claim 1, further comprising compiling the processingparameter into a processing parameters map.
 9. The method of claim 1,wherein the processing window limiting pattern is identified using anempirical model or a computational model.
 10. The method of claim 1,wherein the processing parameter is any one or more selected from:focus, dose, a source parameter, a projection optics parameter, dataobtained from metrology, and/or data from an operator of a processingapparatus used in the device manufacturing process.
 11. The method ofclaim 1, wherein the determining or predicting the existence, theprobability of existence, the characteristic, or the combinationthereof, of the defect comprises simulating an image or expectedpatterning contours of the processing window limiting pattern under theprocessing parameter and determining an image or contour parameter. 12.A method of manufacturing a device involving processing a pattern onto asubstrate or onto a die of the substrate, the method comprising:determining a processing parameter before processing the substrate orthe die; predicting or determining existence of a defect, probability ofexistence of a defect, a characteristic of a defect, or a combinationthereof, using the processing parameter before processing the substrateor the die, and using a characteristic of the substrate or the die, acharacteristic of the geometry of a pattern to be processed onto thesubstrate or the die, or both; adjusting the processing parameter basedon the prediction or determination so as to eliminate, reduce theprobability of or reduce the severity of, the defect.
 13. A method ofmanufacturing a device comprising: the computer-implemented defectprediction method according to claim 1; and indicating which ofplurality of processing window limiting patterns to inspect at leastpartially based on the determined or predicted existence, probability ofexistence, characteristic, or the combination thereof, of the defect.14. A defect determination, prediction or classification method for alithographic process, wherein the method comprises a step of determiningor predicting existence, probability of existence, a characteristic, ora combination thereof, of a defect using a simulation of at least a partof the lithographic process.
 15. The defect determination or predictionmethod according to claim 14, wherein the lithographic process comprisesa device manufacturing process involving processing a pattern onto asubstrate, the determined or predicted existence, probability ofexistence, characteristic, or combination thereof, of the defect beingpart of the pattern.
 16. The defect determination or prediction methodaccording to claim 15, wherein the defect is determined or predictedbefore the pattern is irreversibly processed onto the substrate.
 17. Amethod of selecting a pattern to be inspected from a plurality ofpatterns in a lithographic process, the method comprising a step ofselecting the pattern to be inspected at least partially based on asimulation of at least a part of the lithographic process.
 18. Themethod according to claim 17, wherein the lithographic process comprisesa device manufacturing process involving processing the plurality ofpatterns onto a substrate.
 19. The method according to claim 17, whereinthe selected pattern is inspected to assess whether the selected patternis defective or whether a part of the selected pattern comprises adefect.
 20. A computer program product comprising a computer readablemedium having instructions recorded thereon, the instructions whenexecuted by a computer implementing the method of claim 1.